How Does DeepSeek Think? A Look at Its Reasoning Chain in Python

How Does DeepSeek Think? A Look at Its Reasoning Chain in Python 

AI models often generate answers without showing how they arrive at their conclusions. DeepSeek, however, provides a visible reasoning chain, allowing users to see the intermediate steps before the final answer. In this post, we analyze how DeepSeek tackles a simple Python problem and break down its thought process.

The Prompt
We gave DeepSeek the following Python-related prompt:

"Write a Python function that takes a list of numbers and returns a new list with only the even numbers."

Prompt:  
Write a Python function that takes a list of numbers and returns 
a new list with only the even numbers.
DeepSeek’s Full Response (Reasoning Chain + Final Answer):

Okay, I need to write a Python function that takes a list of 
numbers and returns a new list containing only the even numbers. 
Let me think about how to approach this.

First, I remember that even numbers are those divisible by 2, 
so checking if a number modulo 2 equals zero would work. 
So for each number in the input list, I can check if num % 2 == 0. 
If yes, include it in the new list.

Now, how to create the new list. I can initialize an empty list 
and iterate through each element in the input list. For each element, 
perform the check and append it to the new list if it's even.

Alternatively, using list comprehension might be more concise. 
The list comprehension would loop through each number in the 
input list and include it in the new list only if the condition is met.

So the function could be something like:

def get_even_numbers(numbers):
return [num for num in numbers if num % 2 == 0]

Wait, but what about edge cases? Like if the input list is empty, 
this should return an empty list, which it does. What if there are 
negative numbers? Well, the modulo operation works for negatives as well, 
so -2 % 2 is 0, so that's handled correctly.

Another thing: what if the input list contains non-integer numbers, 
like floats? The problem statement says "list of numbers," so presumably 
integers and floats are allowed. For example, 4.0 is even if it's an 
integer, but 4.0 is technically a float here. Wait, but 4.0 % 2 is 0.0, 
so the condition would be checking if 0.0 equals 0. Which it does in 
Python when compared with ==. So even if a number is a float, like 4.0, 
it would still be considered even. But the user might expect only integers. 

Hmm, but the question says "numbers," so maybe it's okay. Unless the 
problem specifically wants to filter out non-integer even numbers. 
But the problem statement doesn't mention that. So perhaps we don't need 
to worry about that. The function as written will include any number 
(int or float) that is even.

So, the function seems correct. Let me test a few examples.

Test case 1: Input [1,2,3,4,5], output should be [2,4]. The function 
returns that.

Test case 2: Input [], returns [].

Test case 3: Input [2,4,6], returns same.

Test case 4: Input [-2, -3], returns [-2].

Test case 5: Input [0], returns [0] because 0 is even.

Test case 6: Input [2.0, 3.0], returns [2.0], since 2.0 % 2 is 0.0 which 
equals 0.

All these cases should work with the list comprehension approach. 
So the function should be correct.

Analysis of DeepSeek’s Reasoning

Step 1: Identifying the Problem
DeepSeek starts by defining the problem clearly: we need to filter even numbers from a list. This is a solid first step, as it shows an understanding of the task requirements.

Step 2: Choosing an Approach
DeepSeek considers two main methods:

• A loop with conditionals: Iterating through the list and appending even numbers to a new list.
• List comprehension: A more concise method that filters the list in a single line.

DeepSeek correctly identifies that both approaches are valid but ultimately selects list comprehension as the preferred solution.

Step 3: Handling Edge Cases
A major strength of this reasoning chain is its thoughtfulness about edge cases:

• Empty lists: The function correctly returns an empty list.
• Negative numbers: The modulus operation works correctly for negatives.
• Zero: DeepSeek recognizes that 0 is even.
• Floating-point numbers: It considers whether floats should be included and determines that Python's modulo operation works with them.

This depth of analysis shows that DeepSeek isn’t just pattern-matching responses—it’s simulating logical reasoning.

Alternative Implementations
DeepSeek's final function is:

Python

def get_even_numbers(numbers):
    return [num for num in numbers if num % 2 == 0]

This is efficient and idiomatic Python. However, an alternative approach using a traditional loop might be preferable for beginners:

Python

def get_even_numbers(numbers):
    even_numbers = []
    for num in numbers:
        if num % 2 == 0:
            even_numbers.append(num)
    return even_numbers

This version is more explicit, making it easier for beginners to follow.

Final Thoughts
DeepSeek's visible reasoning chain offers a fascinating look into how AI models think. Unlike other LLMs that provide only a final answer, DeepSeek walks through its thought process, considers edge cases, and questions its own assumptions. For programmers and AI enthusiasts, this kind of transparency is invaluable. It allows us to understand and critique AI logic rather than just taking its answers at face value. What should we test next? Let us know in the comments, and we’ll analyze more AI-generated reasoning chains! 🚀

Need DeepSeek Expertise?

If you're looking for guidance on DeepSeek challenges or want to collaborate, feel free to reach out! We'd love to help you tackle your DeepSeek projects. 🚀

Email us at: info@pacificw.com

Image: Gemini